Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma

Abstract

Background: Lung adenocarcinoma (LUAD) is one of the most lethal malignancies and is currently lacking in effective biomarkers to assist in diagnosis and therapy. The aim of this study is to investigate hub genes and develop a risk signature for predicting prognosis of LUAD patients. Methods: RNA-sequencing data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes associated with mRNA expression-based stemness indices (mRNAsi) in TCGA. We utilized LASSO Cox regression to assemble our predictive model. To validate our predictive model, me applied it to an external cohort. Results: mRNAsi index was significantly associated with the tissue type of LUAD, and high mRNAsi scores may have a protective influence on survival outcomes seen in LUAD patients. WGCNA indicated that the turquoise module was significantly correlated with the mRNAsi. We identified a 9-gene signature (CENPW, MCM2, STIL, RACGAP1, ASPM, KIF14, ANLN, CDCA8, and PLK1) from the turquoise module that could effectively identify a high-risk subset of these patients. Using the Kaplan-Meier survival curve, as well as the time-dependent receiver operating characteristic (tdROC) analysis, we determined that this gene signature had a strong predictive ability (AUC = 0.716). By combining the 9-gene signature with clinicopathological features, we were able to design a predictive nomogram. Finally, we additionally validated the 9-gene signature using two external cohorts from GEO and the model proved to be of high value. Conclusion: Our study shows that the 9-gene mRNAsi-related signature can predict the prognosis of LUAD patient and contribute to decisions in the treatment and prevention of LUAD patients.

ScRNA-seq and bulk RNA-seq reveal the characteristics of ferroptosis and establish a risk signature in cholangiocarcinoma

“…42 BNIP3 and CENPW have also been reported as prognostic predictors in various cancers. 43 , 44 , 45 These studies confirm the rationality and importance of the prognostic signature in this study.…”

ASPM induces radiotherapy resistance by disrupting microtubule stability leading to chromosome malsegregation in non-small cell lung cancer

“…Indeed, high expression of ASPM correlated directly with chromosome missegregation and increased numbers of micronuclei, suggesting that high expression of ASPM in NSCLC is more likely to be intrinsic and integral to RT resistance rather than incidental. This notion is also supported by recent comprehensive functional analyses of ASPM in different types of tumors in patients [39][40][41]. We propose that mis-segregation of chromosomes could have much broader effects extending beyond radiation resistance in cancer, which underscores the broad clinical value of understanding the exact role of the genomic instability in cancer progression and therapy.…”

The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM

“…multiple ASPM mutations (Table 2) in tumors. 2 Meanwhile, high levels of ASPM expression correlate with poor prognosis in various types of cancer (Figure 3B), including bladder cancer (Chen et al, 2019(Chen et al, , 2021 2 https://cancer.sanger.ac.uk/cosmic Gao et al, 2020;Liu et al, 2023), prostate cancer (Xie et al, 2017;Pai et al, 2019;Xu et al, 2019), breast cancer (Shubbar et al, 2013;Tang et al, 2019;Wei et al, 2021;Wang et al, 2022), triple-negative breast cancer , esophageal cancer (ESCA; , hepatocellular carcinoma (Lin et al, 2008;Li and Xu, 2020;Yang et al, 2021;Hu et al, 2022;Qiao et al, 2022;Tan et al, 2022;Hasan et al, 2023;Hossen et al, 2023Hossen et al, ), 10.3389/fnins.2023 Frontiers in Neuroscience 13 frontiersin.org glioblastoma (Visnyei et al, 2011;Qin et al, 2023), epithelial ovarian cancer (Brüning-Richardson et al, 2011;Alsiary et al, 2014;, osteosarcoma (Liu et al, 2021), endometrial carcinoma , malignant pleural mesothelioma (Zhang et al, 2020), cervical squamous cell carcinoma (Wen et al, 2020), lung adenocarcinoma (Feng et al, 2021;Hou et al, 2022;Tang et al, 2022;Yin et al, 2022;, anaplastic thyroid carcinoma (Fang et al, 2023), cutaneous squamous cell carcinoma (Su et al, 2022), human sarcomas …”